To this end, we introduce a random shuffling method in the deepfake detection task to enhance the robustness of the detection network and propose a novel deepfake detection framework named Block Shuffling Learning (BSL). The framework consists of the intra-block shuffling learning module and the inter-block shuffling learning module. For the former one, we design a random transformation to destroy the local structure. Occlusion of a local area is a data enhancement method  [1]}, [2]} to improve the robustness of the network. Random shuffling can prevent the network from extracting features [1]}, which has the same effect as occluding local areas. Unlike just filling the occluded regions with random values or regional averages, the method we propose retains the structure-independent features of the region. The structure-independent features of the two images are more likely to be brought into the synthesized image, such as illumination, color temperature, and some statistical features. For the noise mode introduced by intra-block shuffling, we design an adversarial loss to alleviate over-fitting problem. For the latter one, we divide the input image into blocks and randomly shuffle blocks to destroy the global structure. Then we make the neural network establish the correlation among the blocks through reconstruction  [4]}. Destroying the global structure is aimed at encourage the network to extract features from local regions. Reconstruction is the opposite way of destruction. The semantic correlation among local regions are modeled by restoring the original regional arrangement.
